Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification

• Published in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 6489 - 6498
• Main Authors: Zhouy, Yi, Shao, Ling
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2018
• Subjects: Extraterrestrial measurements; Feature extraction; Image color analysis; Task analysis; Training; Visualization
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2018.00679

Vehicle re-identification (re-ID) has the huge potential to contribute to the intelligent video surveillance. However, it suffers from challenges that different vehicle identities with a similar appearance have little inter-instance discrepancy while one vehicle usually has large intra-instance differences under viewpoint and illumination variations. Previous methods address vehicle re-ID by simply using visual features from originally captured views and usually exploit the spatial-temporal information of the vehicles to refine the results. In this paper, we propose a Viewpoint-aware Attentive Multi-view Inference (VAMI) model that only requires visual information to solve the multi-view vehicle reID problem. Given vehicle images of arbitrary viewpoints, the VAMI extracts the single-view feature for each input image and aims to transform the features into a global multiview feature representation so that pairwise distance metric learning can be better optimized in such a viewpointinvariant feature space. The VAMI adopts a viewpoint-aware attention model to select core regions at different viewpoints and implement effective multi-view feature inference by an adversarial training architecture. Extensive experiments validate the effectiveness of each proposed component and illustrate that our approach achieves consistent improvements over state-of-the-art vehicle re-ID methods on two public datasets: VeRi and VehicleID.